The McMaster Monitoring My Mobility (MacM3) study aims to understand trajectories of mobility decline in later life using multisensor wearable technology. To our knowledge, MacM3 is the first major cohort to combine accelerometry and a Global Positioning System (GPS) to track real-world mobility in community-dwelling older adults.

Between May 2022 and May 2024, MacM3 recruited 1555 community-dwelling older adults (mean age 73.9 years, SD=5.5) from Hamilton and Toronto, Ontario. Of the cohort, 68.4% were female, 62.4% married/partnered, 75.3% had post-secondary education and 62.9% had≥3 comorbidities. Most were Canadian born (69.4%) and white/Caucasian (88.0%), with greater ethnocultural diversity observed at the Toronto site.

At baseline, 56.7% of participants reported no mobility limitations, 15.9% had preclinical limitations and 27.4% had minor mobility limitations. Mean gait speed for the total sample was 1.23 m/s, with a mean Timed Up and Go time of 9.4 s and a 5x sit-to-stand time of 13.0 s. A total of 1301 participants had valid wrist-worn device data, and 1008 participants who agreed to wear the thigh-worn device had valid data (≥7 days with ≥10 hours of wear per day). Step count data (n=1008) revealed a mean of 8437 steps per day (SD=2943), with 5073 steps in the lowest quartile and 12 303 steps in the highest.

Ongoing work aims to develop predictive models of mobility decline by integrating wearable, clinical and environmental data. Pipeline enhancements will enable GPS/inertial measurement unit fusion to explore mobility-environment interactions and support ageing-in-place tools.

Real-world effectiveness of a new treatment is relevant information for patients, healthcare professionals and payers, especially when patients encountered in routine clinical care differ significantly from those recruited in the randomised controlled trials (RCTs) that led to approval. However, obtaining effect estimates can be challenging when a new drug has only recently been marketed and real-world data (RWD) are not yet available. For new breast cancer (BC) therapies, we illustrate how RCT inferences can be transported to a target population and how a synthetic population can be generated to mimic a target population for which no RWD is yet available.

In our framework, we defined the data-generating process for the RCT population and the real-world (target) population with confounders, effect-modulating covariates and survival times as outcomes. First, we conducted generalisability and transportability (G&T) analyses to transport the RCT results to the simulated target population, applying the inverse probability of sampling weighting and outcome model-based estimator approach. We then used Synthea to generate a synthetic target population based on German BC survival rates and combined both approaches into a coherent strategy.

Effect estimates (HRs with 95% CIs) transported from the RCT to our defined target population closely matched the expected real-world effect (RCT: 0.68 (0.65; 0.71); real-world: 0.75 (0.71; 0.79); transported from RCT: 0.76 (0.71; 0.81)). BC survival rates were very similar between observed and synthetic data (prediction error in absolute survival rates: 1.62%).

Combining G&T with synthetic data may inform decision-making in situations where RWD are not (yet) available.